Cited By
View all- Xu XShan DWang GJiang X(2016)Multimodal medical image fusion using PCNN optimized by the QPSO algorithmApplied Soft Computing10.1016/j.asoc.2016.03.02846:C(588-595)Online publication date: 1-Sep-2016
A Modified Quantum-Behaved Particle Swarm Optimization
Authors: 
Jun Sun, C. -H. Lai, Wenbo Xu, Yanrui Ding, Zhilei ChaiAuthors Info & Claims
Pages 294 - 301
Abstract
Based on the previously introduced Quantum-behaved Particle Swarm Optimization (QPSO), a revised QPSO with Gaussian disturbance on the mean best position of the swarm is proposed. The reason for the introduction of this novel method is that the disturbance can effectively prevent the stagnation of the particles and therefore make them escape the local optima and sub-optima more easily. Before proposing the Revised QPSO (RQPSO), we introduce the origin and the development of the original PSO and QPSO. To evaluate the performance of the new method, the Revised QPSO, along with QPSO and Standard PSO, is tested on several well-known benchmark functions. The experimental results show that the Revised QPSO has better performance than QPSO and Standard PSO generally.
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- A Modified Quantum-Behaved Particle Swarm Optimization
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Published In
May 2007
1253 pages
ISBN:9783540725831
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 27 May 2007
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View all- Xu XShan DWang GJiang X(2016)Multimodal medical image fusion using PCNN optimized by the QPSO algorithmApplied Soft Computing10.1016/j.asoc.2016.03.02846:C(588-595)Online publication date: 1-Sep-2016